A probabilistic model for API contract specification retrieval focusing on the openAPI standard.
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| Title: | A probabilistic model for API contract specification retrieval focusing on the openAPI standard. |
|---|---|
| Authors: | Moon, Saeyoung1 (AUTHOR), Kerr, Gregor1 (AUTHOR), Silavong, Fran1 (AUTHOR), Moran, Sean1 (AUTHOR) sean.j.moran@jpmchase.com |
| Source: | Data Mining & Knowledge Discovery. Jan2025, Vol. 39 Issue 1, p1-24. 24p. |
| Subjects: | Recommender systems, Log-linear models, Best practices, Engines, Databases, Feature extraction |
| Abstract: | Designing a new API for a large-scale project requires developers to make strategic design choices that will allow the codebase to evolve sustainably. To create well-structured API components, developers can learn from existing APIs. However, the lack of standardized methods for comparing API designs often makes this learning process inefficient and challenging. To bridge this gap, we introduce API-Miner, which, to our knowledge, is one of the first engines to recommend API-to-API specifications. API-Miner retrieves relevant components from OpenAPI specifications-a widely recognized standard for describing web APIs. The engine introduces several key innovations: (1) novel techniques for processing and extracting critical information from OpenAPI specifications, (2) specialized feature extraction methods tailored to the technical domain of API specifications, and (3) a log-linear probabilistic model that integrates multiple signals to retrieve relevant and high-quality OpenAPI components based on a query specification. Through both quantitative and qualitative evaluations, API-Miner achieves a recall@1 of 91.7% and an F1 score of 56.2%, outperforming baseline models by 15.4 percentage points (pp) in recall@1 and 3.2 pp in F1. API-Miner enables developers to access relevant OpenAPI components from public or internal databases early in the API development cycle, facilitating the learning of best practices and the identification of potential redundancies. This tool helps streamline the development process and supports the creation of maintainable, high-quality APIs. The code for API-Miner is available at https://github.com/jpmorganchase/api-miner. [ABSTRACT FROM AUTHOR] |
| Copyright of Data Mining & Knowledge Discovery is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
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| Header | DbId: egs DbLabel: Engineering Source An: 180904670 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: A probabilistic model for API contract specification retrieval focusing on the openAPI standard. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Moon%2C+Saeyoung%22">Moon, Saeyoung</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Kerr%2C+Gregor%22">Kerr, Gregor</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Silavong%2C+Fran%22">Silavong, Fran</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Moran%2C+Sean%22">Moran, Sean</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> sean.j.moran@jpmchase.com</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Data+Mining+%26+Knowledge+Discovery%22">Data Mining & Knowledge Discovery</searchLink>. Jan2025, Vol. 39 Issue 1, p1-24. 24p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Recommender+systems%22">Recommender systems</searchLink><br /><searchLink fieldCode="DE" term="%22Log-linear+models%22">Log-linear models</searchLink><br /><searchLink fieldCode="DE" term="%22Best+practices%22">Best practices</searchLink><br /><searchLink fieldCode="DE" term="%22Engines%22">Engines</searchLink><br /><searchLink fieldCode="DE" term="%22Databases%22">Databases</searchLink><br /><searchLink fieldCode="DE" term="%22Feature+extraction%22">Feature extraction</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Designing a new API for a large-scale project requires developers to make strategic design choices that will allow the codebase to evolve sustainably. To create well-structured API components, developers can learn from existing APIs. However, the lack of standardized methods for comparing API designs often makes this learning process inefficient and challenging. To bridge this gap, we introduce API-Miner, which, to our knowledge, is one of the first engines to recommend API-to-API specifications. API-Miner retrieves relevant components from OpenAPI specifications-a widely recognized standard for describing web APIs. The engine introduces several key innovations: (1) novel techniques for processing and extracting critical information from OpenAPI specifications, (2) specialized feature extraction methods tailored to the technical domain of API specifications, and (3) a log-linear probabilistic model that integrates multiple signals to retrieve relevant and high-quality OpenAPI components based on a query specification. Through both quantitative and qualitative evaluations, API-Miner achieves a recall@1 of 91.7% and an F1 score of 56.2%, outperforming baseline models by 15.4 percentage points (pp) in recall@1 and 3.2 pp in F1. API-Miner enables developers to access relevant OpenAPI components from public or internal databases early in the API development cycle, facilitating the learning of best practices and the identification of potential redundancies. This tool helps streamline the development process and supports the creation of maintainable, high-quality APIs. The code for API-Miner is available at https://github.com/jpmorganchase/api-miner. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Data Mining & Knowledge Discovery is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1007/s10618-024-01073-4 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 24 StartPage: 1 Subjects: – SubjectFull: Recommender systems Type: general – SubjectFull: Log-linear models Type: general – SubjectFull: Best practices Type: general – SubjectFull: Engines Type: general – SubjectFull: Databases Type: general – SubjectFull: Feature extraction Type: general Titles: – TitleFull: A probabilistic model for API contract specification retrieval focusing on the openAPI standard. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Moon, Saeyoung – PersonEntity: Name: NameFull: Kerr, Gregor – PersonEntity: Name: NameFull: Silavong, Fran – PersonEntity: Name: NameFull: Moran, Sean IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 01 Text: Jan2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 13845810 Numbering: – Type: volume Value: 39 – Type: issue Value: 1 Titles: – TitleFull: Data Mining & Knowledge Discovery Type: main |
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